Curve fitting method

Abstract

The invention relates to a curve fitting method. The curve fitting method includes the following steps of determining an optimized target function and a target parameter, generating the optimal solution of the target parameter through a genetic algorithm, setting the initial food concentration for an artificial fish school algorithm and the food concentration for the optimal solution according to the obtained optimal solution of the target parameter, calculating a concentration function of food around each artificial fish, recording the optimal solution on a call board, and obtaining the optimized optimal value of the curve fitting. According to the curve fitting method, the convergence speed is increased through efficient convergence and positive feedback of artificial fish schools, and the phenomena that convergence is slow and precocity occurs when a traditional genetic algorithm is adopted are avoided.

Description

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AI Technical Summary

Benefits of technology

This technology uses two techniques - one involves combining different types of data from multiple sources into an optimized solution for better performance or efficiency. One technique called genetic programming (GPM) allows us to optimize certain aspects of natural selection by adjusting various factors like environmental conditions during evolution. Another method called Artificial Fish School Algorithm (AFSA), also known as quantum computing, helps make it faster than previous methods but still works well even if there are many variables involved at once. Overall this approach improves both accuracy and effectiveness over existing approaches.

Problems solved by technology

Technological Problem: Current Solar Cell Testing Methods have limitations such as requiring multiple measurements or complex calculations that make them time-consuming and expensive. Additionally, they often require specific conditions like light intensity and temperature during measurement, making their effectiveness uncertain due to variations over time.

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